Abstract

Precision agriculture can in many cases increase the profitability and sustainability of farming. Current precision ag recommendations are often based on empirical models, such as linear or curvilinear relationships between measurement and recommended application rate. For further increases in profitability and sustainability, mechanistic models of crop growth are needed. We investigated whether crop and soil parameters in these models can be determined via inverse modelling using readily available “big data” obtained from soil scans, remote sensing and yield monitors. Moreover, we investigated whether soil parameters determined in this way express within-field spatial variation in these measurements. We used data from several commercial potato growers in the period 2014-2018 (approx. 30 site-years). Initial results show that variations between fields can be parameterized. Accounting for within-field spatial variability through the soil parameters of the fields in question is considerably more difficult.

abstract = "Precision agriculture can in many cases increase the profitability and sustainability of farming. Current precision ag recommendations are often based on empirical models, such as linear or curvilinear relationships between measurement and recommended application rate. For further increases in profitability and sustainability, mechanistic models of crop growth are needed. We investigated whether crop and soil parameters in these models can be determined via inverse modelling using readily available “big data” obtained from soil scans, remote sensing and yield monitors. Moreover, we investigated whether soil parameters determined in this way express within-field spatial variation in these measurements. We used data from several commercial potato growers in the period 2014-2018 (approx. 30 site-years). Initial results show that variations between fields can be parameterized. Accounting for within-field spatial variability through the soil parameters of the fields in question is considerably more difficult.",

N2 - Precision agriculture can in many cases increase the profitability and sustainability of farming. Current precision ag recommendations are often based on empirical models, such as linear or curvilinear relationships between measurement and recommended application rate. For further increases in profitability and sustainability, mechanistic models of crop growth are needed. We investigated whether crop and soil parameters in these models can be determined via inverse modelling using readily available “big data” obtained from soil scans, remote sensing and yield monitors. Moreover, we investigated whether soil parameters determined in this way express within-field spatial variation in these measurements. We used data from several commercial potato growers in the period 2014-2018 (approx. 30 site-years). Initial results show that variations between fields can be parameterized. Accounting for within-field spatial variability through the soil parameters of the fields in question is considerably more difficult.

AB - Precision agriculture can in many cases increase the profitability and sustainability of farming. Current precision ag recommendations are often based on empirical models, such as linear or curvilinear relationships between measurement and recommended application rate. For further increases in profitability and sustainability, mechanistic models of crop growth are needed. We investigated whether crop and soil parameters in these models can be determined via inverse modelling using readily available “big data” obtained from soil scans, remote sensing and yield monitors. Moreover, we investigated whether soil parameters determined in this way express within-field spatial variation in these measurements. We used data from several commercial potato growers in the period 2014-2018 (approx. 30 site-years). Initial results show that variations between fields can be parameterized. Accounting for within-field spatial variability through the soil parameters of the fields in question is considerably more difficult.